sampling

This project compares different sampling techniques when used upon various ML models for classification: following sampling techniques were used:

1. Random Sampling
2. Structured Sampling
3. Stratified sampling
4. cluster sampling
5. Weighted sampling

for the following ML models

1. logistic regression
2. Random forest classifier
3. Naive bayes
4. SVM
5. Decision Tree classifier

Run the file Main.py with the given dataset to find out which sampling is best for the following models PS: Oversampling using SMOTE was applied prior to applying these techniques.